The provided code represents a computational model of synaptic transmission incorporating short-term synaptic plasticity mechanisms, specifically facilitation and depression, as found in the primary visual cortex of rats. It is based on the model described by Varela et al. (1997), which quantitatively accounts for the dynamics of short-term synaptic plasticity at excitatory synapses. ### Biological Basis of the Model: #### Synaptic Kinetics: - **Two-State Kinetic Scheme**: The synaptic conductance is described using a bi-exponential model, involving two time constants, `tau1` (rise time) and `tau2` (decay time). Biologically, these represent the kinetics of neurotransmitter binding and the subsequent conductance change due to receptor activation. The decay time (`tau2`) is required to be greater than the rise time (`tau1`), reflecting the natural tendency for synaptic currents to rise quickly and decay more slowly. #### Short-Term Synaptic Plasticity: - **Facilitation (`F`)**: Facilitation refers to a temporary increase in synaptic strength occurring due to closely timed successive synaptic activations. This model uses a facilitation parameter (`f`) and a facilitation time constant (`tau_F`) to account for this effect. Facilitation is modeled as an increase in synaptic efficacy that decays exponentially over time. - **Depression (`D1` and `D2`)**: Depression represents a decrease in synaptic strength following repetitive synaptic activity. This model incorporates both fast (`D1`) and slow (`D2`) components of synaptic depression, characterized by their respective time constants (`tau_D1`, `tau_D2`) and depression parameters (`d1`, `d2`). These parameters capture the reduction in neurotransmitter release probability or receptor availability due to recent synaptic activity. #### Synaptic Inputs and Weighting: - The model allows for multiple streams of synaptic input, each maintaining its own history of synaptic state (facilitation and depression). This reflects the biological reality of synapses receiving inputs from multiple neurons, each with distinct patterns of activity. #### Conductance Change: - **Point Process**: The synaptic conductance change is modeled as a `POINT_PROCESS` called `FDSExp2Syn`, which generates a current based on the voltage difference and conductance state. The synaptic current (`i`) is computed from the instantaneous conductance (`g`), derived from the difference between the states `A` and `B`. By modeling these aspects, the code simulates how excitatory synapses in the brain exhibit dynamic changes in response to spike activity, incorporating both facilitation and depression mechanisms. This is crucial for understanding temporal aspects of information processing and transmission in neural circuits.